Contents 
#import sys
#print(sys.executable)

#!python --version

#!pip install GDAL-3.4.2-cp38-cp38-win_amd64.whl

#!pip install Fiona-1.8.21-cp38-cp38-win_amd64.whl

#!pip install pyproj-3.3.1-cp38-cp38-win_amd64.whl

#!pip install Shapely-1.8.1.post1-cp38-cp38-win_amd64.whl

#!pip install geopandas

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from urllib.request import urlopen
import json
import geopandas as gpd
from IPython.core.display import display, HTML
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
config={'showLink': False, 'displayModeBar': False}

init_notebook_mode(connected=True)

Import data and preprocess

data = pd.read_csv("NYCgov_Poverty_Measure_Data__2015_.csv")

features = ['SERIALNO', 'SPORDER', 'AGEP', 'CIT', 'REL', 'SCH',
       'SCHG', 'SCHL', 'SEX', 'ESR', 'LANX', 'ENG', 'MSP',
       'WKHP', 'DIS', 'JWTR', 'NP', 'TEN', 'HHT', 'AgeCateg', 'Boro',
       'CitizenStatus', 'EducAttain', 'Ethnicity', 'FamType_PU', 'FTPTWork', 
       'INTP_adj', 'MRGP_adj', 'NYCgov_Income', 'NYCgov_Pov_Stat', 'NYCgov_REL',
       'NYCgov_Threshold', 'Off_Pov_Stat', 'Off_Threshold', 'OI_adj', 'PA_adj', 
       'Povunit_ID', 'Povunit_Rel', 'PreTaxIncome_PU', 'RETP_adj', 'RNTP_adj', 
       'SEMP_adj', 'SSIP_adj', 'SSP_adj', 'TotalWorkHrs_PU', 'WAGP_adj']

#Recode = code in dictionary
# CIT: Citenzenship
# REL: is relationship ie. Daughter, Son, etc. is ACS code ()
# SCH, SCHG: (SCHG is ACS code) for educaiton
# SCHL: Education attainment ACS code
# ESR: Employement status (code in dictionary file)
# LANX: language other than language spoken 
# ENG: ability to speak english
# MSP: Married or not (code in dictionary file)
# MAR: Marital status 
# WKHP: huors work per week
# DIS: disability (Recode)
# JWTR: transportation to work (ACS)
# NP: number of people in household 
# TEN: Housing tenure
# FamType_PT: PovertyUnit familytype (umiddelbart fjerne)
# FTPTWork: work experience (recode)
# INTP_adj: Income adjusted
# MRGP_adj: Morgage amount adjusted
# SEMP_adj: self employed
# SSIP_adh: supplementary income 
# SSP_adj: social socurity income (people who are disabled)
# WAGP_adj: Wages

Visulization

  • Number of healty tree in each district

  • Probablity of healthy tree in each district

  • histogram of diameter

  • histogram of depth

  • plot of location for trees (heatmap)

X = data[features]

X_grouped = X.groupby(['Boro']).median()

gdf = gpd.read_file('https://raw.githubusercontent.com/dwillis/nyc-maps/master/boroughs.geojson')
gdf.to_crs(epsg=4326, inplace=True)
gdf.set_index('BoroName', inplace=True)
gdf['BoroCode'] = [5,4,2,3,1]
gdf.sort_index(inplace=True)

X_grouped['BoroName'] = ['Bronx','Brooklyn','Manhattan','Queens','Staten Island']
X_grouped.set_index('BoroName',inplace=True)

att = 'PreTaxIncome_PU'

fig = px.choropleth_mapbox(X_grouped[att].reset_index(), geojson=gdf['geometry'], locations=gdf.index, color='PreTaxIncome_PU',
                           color_continuous_scale="Viridis",
                           range_color=(X_grouped[att].min(),X_grouped[att].max()),
                           mapbox_style="carto-positron",
                           zoom=8.5, center = {"lat": 40.730610, "lon": -73.935242},
                           opacity=0.5,
                           labels={att:att}
                          )
plot(fig, filename = 'figure_1.html')
display(HTML('figure_1.html'))

#fig.update_layout(margin={"r":300,"t":100,"l":200,"b":0})
#fig.show("notebook")
#fig.show()

X_grouped[[att,'EducAttain']]
PreTaxIncome_PU EducAttain
BoroName
Bronx 42553.719 2.0
Brooklyn 60075.840 2.0
Manhattan 75094.797 4.0
Queens 70288.734 2.0
Staten Island 90113.758 2.0

attributes in ML model

NP: Number of people in house hold
Race
Sex
Boro
Age
LANX: language other than language spoken
DIS: disability (Recode)

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NYC Education